NDLI: A large-to-fine-scale shape prior for probabilistic segmentations using a deformable m-rep

Content Provider	IEEE Xplore Digital Library
Author	Xiaoxiao Liu Ja-Yeon Jeong Levy, J.H. Saboo, R.R. Chaney, E.L. Pizer, S.M.
Copyright Year	2008
Description	Author affiliation: Med. Image Display&Anal. Group (MIDAG), Univ. of North Carolina, Chapel Hill, NC (Xiaoxiao Liu; Ja-Yeon Jeong; Levy, J.H.; Saboo, R.R.; Chaney, E.L.; Pizer, S.M.)
Abstract	Training a shape prior has been potent scheme for anatomical object segmentations, especially for images with noisy or weak intensity patterns. When the shape representation lives in a high dimensional space, principal component analysis is often used to calculate a low dimensional variation subspace from frequently limited number of training samples. However, the eigenmodes of the sub-space tend to keep the large-scale variation of the shape only, losing the detailed localized variability which is crucial to accurate segmentations. In this paper, we propose a large-to-fine-scale shape prior for probabilistic segmentation to enable local refinement, using a deformable medial representation, called the m-rep. Tests on the goodness of the shape prior are carried out on large simulated data sets of (a) 1000 deformed ellipsoids with mixed global deformations and local perturbation; (b) 500 simulated hippocampus models. The predictability of the shape priors are evaluated and compared by a squared correlations metric and the volume overlap measurement against different training sample sizes. The improved robustness achieved by the large-to-fine-scale strategy is demonstrated, especially for low sample size applications. Finally, posterior 3D segmentations of the bladder from CT images from multiple patients in day-to-day adaptive radiation therapy demonstrate that the local residual statistics introduced by this method improve the segmentation accuracy.
Starting Page	1
Ending Page	8
File Size	868431
Page Count	8
File Format	PDF
ISBN	9781424423392
ISSN	21607508
DOI	10.1109/CVPRW.2008.4563019
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Deformable models Image segmentation Shape measurement Object segmentation Noise shaping Large-scale systems Ellipsoids Hippocampus Principal component analysis Testing
Content Type	Text
Resource Type	Article

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